Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	23,909
2	Arizona	22,805
3	New York	21,466
4	Florida	20,578
5	New Jersey	20,514
6	Mississippi	18,244
7	Rhode Island	17,675
8	District of Columbia	16,925
9	Massachusetts	16,856
10	Alabama	16,798
11	South Carolina	16,335
12	Georgia	15,073
13	Delaware	14,866
14	Nevada	14,606
15	Maryland	14,240
16	Texas	14,234
17	Tennessee	14,103
18	Illinois	13,856
19	Connecticut	13,765
20	Iowa	13,606
21	Arkansas	13,314
22	Nebraska	13,047
23	Utah	12,126
24	California	12,020
25	North Carolina	11,096
26	Idaho	10,809
27	Virginia	10,191
28	Indiana	9,693
29	South Dakota	9,599
30	New Mexico	9,438
31	Wisconsin	9,294
32	Minnesota	9,277
33	Kansas	9,094
34	Pennsylvania	8,914
35	Michigan	8,814
36	Oklahoma	8,531
37	North Dakota	8,063
38	Colorado	7,887
39	Missouri	7,531
40	Washington	7,429
41	Ohio	7,399
42	Kentucky	6,463
43	Puerto Rico	4,959
44	New Hampshire	4,780
45	Alaska	4,576
46	Wyoming	4,473
47	Oregon	4,129
48	West Virginia	3,444
49	Montana	3,266
50	Maine	2,855
51	Vermont	2,251
52	Hawaii	1,224

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	523
2	Florida	426
3	Tennessee	383
4	Mississippi	358
5	Nevada	324
6	Oklahoma	309
7	Alabama	287
8	Georgia	275
9	Arizona	271
10	South Carolina	265
11	Idaho	260
12	Arkansas	243
13	Texas	242
14	Alaska	219
15	Missouri	212
16	California	182
17	North Dakota	177
18	New Mexico	159
19	North Carolina	159
20	Rhode Island	157
21	Nebraska	142
22	Maryland	136
23	Wisconsin	136
24	Puerto Rico	135
25	Virginia	132
26	Utah	130
27	Kansas	117
28	Minnesota	117
29	Washington	117
30	Indiana	110
31	Iowa	110
32	Kentucky	110
33	District of Columbia	107
34	Delaware	103
35	Illinois	103
36	Ohio	94
37	Colorado	91
38	Wyoming	82
39	Michigan	78
40	Pennsylvania	78
41	Oregon	73
42	Montana	72
43	South Dakota	70
44	West Virginia	65
45	New Jersey	53
46	Massachusetts	44
47	Hawaii	32
48	Connecticut	28
49	New York	28
50	New Hampshire	20
51	Maine	11
52	Vermont	4

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,781
2	New York	1,662
3	Connecticut	1,240
4	Massachusetts	1,240
5	Rhode Island	948
6	District of Columbia	826
7	Louisiana	819
8	Michigan	643
9	Illinois	603
10	Delaware	595
11	Maryland	571
12	Pennsylvania	562
13	Mississippi	518
14	Arizona	470
15	Indiana	434
16	Georgia	328
17	Colorado	314
18	Alabama	304
19	South Carolina	303
20	New Hampshire	300
21	New Mexico	298
22	Ohio	289
23	Minnesota	287
24	Florida	284
25	Iowa	265
26	Nevada	246
27	Virginia	245
28	Texas	224
29	California	220
30	Washington	214
31	Missouri	205
32	North Carolina	176
33	Nebraska	168
34	Kentucky	164
35	Wisconsin	157
36	Tennessee	144
37	Arkansas	141
38	South Dakota	139
39	North Dakota	136
40	Oklahoma	128
41	Kansas	118
42	Idaho	94
43	Maine	90
44	Utah	90
45	Vermont	89
46	Oregon	72
47	Puerto Rico	65
48	West Virginia	61
49	Montana	48
50	Wyoming	44
51	Alaska	27
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Texas	17
2	Mississippi	7
3	Arizona	6
4	Louisiana	6
5	South Carolina	6
6	Florida	5
7	Arkansas	3
8	Idaho	3
9	New Mexico	3
10	Alabama	2
11	California	2
12	Georgia	2
13	Missouri	2
14	Nevada	2
15	Ohio	2
16	Illinois	1
17	Indiana	1
18	Iowa	1
19	Kansas	1
20	Kentucky	1
21	Maryland	1
22	Massachusetts	1
23	Montana	1
24	New Jersey	1
25	North Carolina	1
26	Oklahoma	1
27	Oregon	1
28	Tennessee	1
29	Utah	1
30	Washington	1
31	West Virginia	1
32	Alaska	0
33	Colorado	0
34	Connecticut	0
35	Delaware	0
36	District of Columbia	0
37	Hawaii	0
38	Maine	0
39	Michigan	0
40	Minnesota	0
41	Nebraska	0
42	New Hampshire	0
43	New York	0
44	North Dakota	0
45	Pennsylvania	0
46	Puerto Rico	0
47	Rhode Island	0
48	South Dakota	0
49	Vermont	0
50	Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	138,692	1	99
Lake	Tennessee	102,765	2	99
Lee	Arkansas	98,905	3	99
Dakota	Nebraska	94,527	4	99
Buena Vista	Iowa	90,571	5	99
Davidson	Tennessee	29,557	107	96
Richland	South Carolina	17,402	383	87
Orange	California	10,969	836	73
York	South Carolina	10,442	882	71
Pierce	Washington	5,592	1593	49

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	3,027	5	99
Richland	South Carolina	305	665	78
Davidson	Tennessee	279	719	77
Orange	California	183	1003	68
Pierce	Washington	145	1159	63
York	South Carolina	89	1495	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.